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June 30, 2023

Discovering What Sets Car Sickness in Motion with Simulation

Summer is here, inspiring us to make vacation plans. During peak travel times, flying can be expensive, so you may choose to hit the road to get where you’re going instead. But, if the thought of a long car ride makes you sick, Robert Bosch GmbH, the Ferdinand-Steinbeis-Institute, IPG, and Ansys are exploring a possible solution for this well-known problem as a proposed use case that is part of the initiative. This use case is part of a larger objective by to make the software-defined vehicle a reality.

Kinetosis, also known as motion sickness, is a common medical condition that affects an estimated one in three people, mostly women and children. Car sickness is a type of motion sickness that can make you queasy, clammy, or sick to your stomach in reaction to vehicle acceleration or curvy movement. The development of kinetosis as a result of this movement is based on a sensory conflict between visual (sense of sight), proprioceptive (body sensitivity) and vestibular perception (sense of balance).

For example, let’s say you’re a passenger reading a map while on a drive. In this scenario, you see a static image (map), while movements are perceived via your muscles, and the vestibular organ in the inner ear responsible for balance. All of these signals are scrambled as your brain tries to make sense of what’s coming in. In response, you might break out in a cold sweat, or become dizzy or nauseas.

Car sickness

In the Future, Car Sickness Relief Could Be Just a Phone App Away

The premise for a future kinetosis app is based on the anticipation of car sickness related to specific driving maneuvers, and the effect of each maneuver on vehicle occupants. Every action precedes a reaction — in this case, a level of activity that results in car sickness. Each is assigned a value, or level of risk related to car sickness, such as a driver taking a curve too fast.

Different drive styles

This chart presents the kinetosis value over the time of the travel for different driving styles. The kinetosis value rises as the driver approaches the summit on the curvy road. Atop the mountain, the kinetosis level stays nearly constant then rises again on the curvy road down to the valley. The influence of the driving style can be seen not only in the absolute value, but also in the steepness of the different phases.

So, how could this work, exactly? One day, it may be possible to download an app and sync it with a vehicle that would include an artificial intelligence (AI) facial recognition model within the cockpit that determines each occupant’s age and gender. This information could be used to calculate kinetosis levels in response to driver input in various on-road environments.

Any driver activity that puts occupants at risk for kinetosis is then scored, followed by a warning indicator on the infotainment display indicating fresh air is required in the cockpit. These messages include the level of kinetosis an occupant may be experiencing and what corrective action is needed to help reverse it, such as changing driving style or reducing vehicle speed.

Simulation Supports a Digital Playground of Ideas

During development, all of the magic happens in what refers to as its — a recently developed open and web-based prototyping environment that can be used by developers to create and test software-defined vehicle solutions. If a developer has a small machine learning model of something, they can load it up and then try to build an application with an automotive context around it in the playground.

Kinetosis is a good fit for because it provides a data model, including information within that data model that can be used by this machine-learning process to understand various driving outcomes on motion sickness. Developers can test out how the application looks and what it will show in response to specific driving conditions. For example, it’s possible to develop an app for an entertainment system within the playground that can display the kinetosis levels and deliver information connected to specific driving events that correspond with vehicle movement.

So, if you’re in a vehicle riding around curve, you might feel a force pushing you in one direction. That inertia your body is experiencing has physics attached to it that contribute to the level of car sickness you’re experiencing, which is described in the playground. While the playground provided a data model representing this information, it was missing simulation — the key to extracting and analyzing the data coming out of the model.

“As a developer, you can load your app in the playground, and connect it to an interface,” says Lars Kosmann, Senior Application Engineer at Ansys. “You might see a data object that describes acceleration or inertia in one direction, but there is no data behind it, so you cannot directly simulate it. To overcome this challenge, it was necessary to use Ansys simulation to find a way to connect to and provide some relational information that could be viewed in the context of the playground.”

Simulating two different drivers in IPG CarMaker with integrated kinetosis level calculation built in Ansys SCADE Suite. The model is integrated using a functional mockup interface (FMI) as software-in-the-loop provides data from simulation. The cockpit is implemented using SCADE Display to show the current kinetosis level to the driver, which is also integrated as software-in-the-loop using FMI.

Successful Collaboration Leads to Innovation

The data informing app development is coming out of a statistical model built by the Ferdinand-Steinbeis-Institute, covering data related to sudden acceleration, elevation changes, hilly or wooded areas, winding roads, and other factors that contribute to kinetosis. The model also considers the driving styles of an aggressive driver and a normal driver when making predictions about car sickness that will hopefully induce a change in habits behind the wheel.

Anti-Kinetosis is one use case that is exemplary for the co-innovation approach among all initiative partners to make the software-defined vehicle a reality, and foster an open tool chain. For this anti-kinetosis use case, IPG and Ansys relied on Ansys optiSlang process integration, Ansys SCADE suite's model-based development environment and IPG CarMaker, a virtual test-driving solution to build the simulation model that would connect data to the playground.

A close collaboration among all initiative partners enabled a simulation solution that connects both the machine learning model and the driving simulation to the playground. This ensures the successful integration of the resulting data output with

“Right now, despite its potential benefits, there are no immediate plans to integrate this technology into vehicles,” says. Kosman. “It is, however, a test case demonstrating the value of our partnership  as they explore new vehicle concepts within the playground. But, it is possible that this technology may be available in our own vehicles, or as part of an autonomous fleet in the future — whether we’re driving around town or hailing a robotaxi.”

Ansys optiSlang and Ansys SCADE are just a few of our products that address autonomous design and development in automotive applications. Visit our Autonomous Software Development page to learn more.  

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